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The Relationship between Slack Resources and the Performance of

Entrepreneurial Firms: The Role of Venture Capital and Angel Investors

Tom Vanacker

Ghent University

Department of Accountancy and Corporate Finance

Kuiperskaai 55E

9000 Gent, Belgium

Tel: +32 9 264 7960

Fax: +32 9 264 3577

E-mail: TomR.Vanacker@UGent.be

Veroniek Collewaert

Vlerick Business School

Area Entrepreneurship, Governance and Strategy

Reep 1

9000 Gent, Belgium

Tel: +32 9 210 9212

E-mail: veroniek.collewaert@vlerick.com

Ine Paeleman

Ghent University

Department of Accountancy and Corporate Finance

Kuiperskaai 55E

9000 Gent, Belgium

Tel: + 32 9 264 3507

Fax: +32 264 3577

E-mail: Ine.Paeleman@UGent.be

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The Relationship between Slack Resources and the Performance of

Entrepreneurial Firms: The Role of Venture Capital and Angel Investors

ABSTRACT

In this study, we seek to further delineate factors that condition the relationship between slack

resources and firm performance. To do so, we develop and test a model that establishes the role of

venture capital (VC) and angel investors as powerful external stakeholders who positively moderate

the slack-performance relationship. In addition, we provide more insight into this relationship by

examining differences between these two types of private investors and by examining the role of their

ownership stakes. We test our hypotheses using a sample of 1,215 private firms, including VC-

backed firms, angel-backed firms and similar firms without such investors. We find that the presence

of VC investors positively moderates the relationship between both financial and human slack

resources and firm performance, while angel investors only positively moderate the effect of human

resource slack. Further, VC investors are only marginally better at helping entrepreneurs to extract

value from human resource slack than angel investors and they are no better when it comes to

financial slack. Finally, we find that the impact of financial and human resource slack on firm

performance is more positive in VC-backed firms when investors hold high ownership stakes, an

effect which is significantly stronger than when angel investors hold high ownership stakes.

Keywords: entrepreneurship, slack resources, venture capital, angel financing, firm performance

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INTRODUCTION

Whether it is the abundance or scarcity of slack resources that is most beneficial to firm performance

is a heavily debated topic (e.g., Bradley et al., 2011a). Prior research has generally argued for an

inverted U-shaped relationship between slack resources and firm performance to reconcile these

opposite views (Bourgeois, 1981; George, 2005; Nohria and Gulati, 1996; Tan and Peng, 2003).

Nevertheless, simply having resources is not enough to create value out of those resources (Sirmon et

al., 2007). More recently, scholars have therefore devoted increasing attention to when, where and

how slack resources benefit firm performance. For instance, research has shown how the value of

slack is influenced by the environment in which these resources are deployed (Bradley et al., 2011a;

George, 2005). Moreover, how slack resources are managed may also influence their performance

consequences. Simsek et al. (2007), for instance, found that managers’ capability to sense the market

makes them better able to allocate slack towards entrepreneurial actions, which enhances the

performance consequences of these resources.

While these prior studies have been informative in opening up the black box between slack

resources and firm performance, they implicitly assume that managers “go it alone” when managing

their resources. However, managers rarely operate in isolation and often have to deal with powerful

exchange partners who may affect managers’ discretion over resources (Hambrick and Finkelstein,

1987). Venture capital (VC) and angel investors are such partners in entrepreneurial firms; in addition

to providing financial resources in return for part of the ownership of firms, they actively monitor and

influence how their portfolio firms are run after the investment (Mason and Harrison, 1996; Sapienza,

1992; Sapienza et al., 1996). In so doing, these investors may substantially affect entrepreneurs’

discretion over their resources and thus influence how these resources are managed (Hambrick and

Finkelstein, 1987). Using insights from extant slack research and managerial discretion theory, we set

out to examine how powerful exchange partners, such as VC and angel investors, influence the

relationship between slack resources and firm performance.

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Our paper makes three primary contributions. First, we contribute to the literature on slack

resources. Researchers have recently acknowledged that resources do not exploit themselves (Sirmon

et al., 2007). In examining when, where and how value is extracted from slack resources, specific

attention has been paid to the role of industry environments (Bradley et al., 2011a; George, 2005) and

managers (Bradley et al., 2011c; Simsek et al., 2007). Nevertheless, when managing their resources,

entrepreneurs rarely act in isolation. Private investors, such as VC and angel investors, are typically

one of the most important owners in entrepreneurial firms, ranked second behind entrepreneurs

themselves (George et al., 2005). Unfortunately, the role of these private investors in resource value

creation has been largely overlooked. We argue that VC and angel investors -through their active

involvement in entrepreneurial firms- will act as a source of resource value creation. In so doing, we

address an important gap in the literature as identified by Barney and colleagues (2011, p. 1306)

“start-up and growing firms may be wholly owned by their founders or have involvement of angel

investors and venture capitalists. Studies to date have not explicitly compared the different processes

of resource and capability development in these different ownership contexts…” Our study is one of

the first to show how outside investors not only contribute resources as such, but also help in creating

value out of those resources.

Second, we contribute to the literature on managerial discretion. Researchers have examined a

host of factors at the national, industry, firm and individual level that enhance or restrict the internal

discretion of managers (e.g., Carpenter and Golden, 1997; Crossland and Hambrick, 2007;

Finkelstein and Hambrick, 1990; Hambrick and Abrahamson, 1995). Despite this rich literature,

Quigley and Hambrick (2012) recently noted that little attention has been paid to the idea that

powerful parties might influence managerial discretion. This is especially the case for powerful

external parties, such as VC and angel investors. Our study suggests that managerial discretion is

distributed across organizational boundaries, with powerful external parties that are not managers of

the firm.1 Moreover, contrary to that the general assumption that powerful parties may restrict

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discretion (e.g., Hambrick and Finkelstein, 1987), we argue and show that these external parties may

also enhance discretion, something which was only briefly suggested by Thompson (1967).

Finally, our study contributes to governance research. Researchers have long studied the

relationship between ownership and firm performance, especially in established and new public firms,

but mixed findings have emerged (Dalton et al., 2003). We do not focus on the direct relationship

between ownership and firm performance, but rather study how different private investors (VC versus

angel investors) with different ownership stakes (high versus low) influence resource value creation

in the context of private firms. While prior research has argued that different types of owners may

have different goals for investing (Zahra, 1996) and different preferences regarding the allocation of

slack resources (Kim et al., 2008) this evidence again comes from investors in public firms. Our study

highlights some important differences between different types of private investors, challenging the

basic assumption that outside blockholders of equity (as a homogeneous group) by definition have

both the incentive and influence to affect firm decision making (Dalton et al., 2003). We provide new

insights into the role of investor professionalism, ownership and their joint effects on the performance

consequences of slack resources in private firms.

THEORY

Slack Resources, Firm Performance and Managerial Discretion

Slack resources are defined as “potentially utilizable resources that can be diverted or redeployed for

the achievement of organizational goals” (George, 2005, p. 661). Most of our insights regarding the

consequences of slack come from samples of public firms (Bromiley, 1991; Danneels, 2008;

Greenley and Oktemgil, 1998; Kim et al., 2008; Mishina et al., 2004), multinational organizations and

their divisions (Nohria and Gulati, 1996; 1997) or established privately-held firms (George, 2005;

Mellahi and Wilkinson, 2010; Simsek et al., 2007). It is only more recently that scholars have studied

slack in young, small entrepreneurial firms as we do in this study (Bradley et al., 2011a,b; Patzelt et

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al., 2008). While entrepreneurial firms are often portrayed as being resource constrained, this does not

imply that slack cannot be present in these firms. First, entrepreneurial firms differ significantly in

their early resource endowments (Shane and Stuart, 2002). Second, because all resources controlled

by a firm are rarely ever fully utilized, there is always some slack (Bradley et al., 2011b).

Although slack resources vary in type, the majority of previous studies have focused on

financial slack and more specifically the level of liquid assets in excess of those needed for basic

operating expenses (e.g., Bradley et al., 2011a,b; George, 2005; Kim et al., 2008). In entrepreneurial

firms, financial slack may result from early operations as well as from the initial stock of capital

(Bradley et al., 2011a). A relatively small but growing stream of research has also focused on human

resource slack, which refers to the number of employees in excess of those needed for operational

demands (e.g., Mellahi and Wilkinson, 2010; Mishina et al., 2004; Welbourne et al., 1999). We focus

on both types of resource slack because both financial and human resources are critical for the

emergence and development of entrepreneurial firms (Cooper et al., 1994).

Multiple theoretical perspectives have been advanced to understand the performance

consequences of slack resources, but opposing insights have emerged. On the one hand, behavioral

theorists argue that slack resources will increase experimentation, innovation and risk-taking

(Bromiley, 1991; Cyert and March, 1963; George, 2005). Slack allows firms to experiment with new

projects, such as introducing products and entering markets (Hambrick and Snow, 1977). Although

such projects may be risky, they are critical for the performance of entrepreneurial firms (Sapienza et

al., 2006; Zahra, 1995). Moreover, slack shields firms from environmental turbulence (O’Brien,

2003). On the other hand, organizational economists argue that slack is detrimental for firm

performance as it reduces the discipline that is exercised in the selection, support and termination of

investment projects (Nohria and Gulati, 1997). Additionally, resource constraint theorists argue that

slack decreases entrepreneurial ingenuity (Baker and Nelson, 2005; Mosakowski, 2002). Finally,

slack may make entrepreneurs complacent and overly optimistic (Kim et al., 2008). Debruyne et al.

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(2010), for instance, show that the presence of more financial resources makes managers believe they

are able to react effectively to competitive attacks, but also makes them less motivated to do so.2

Scholars have reconciled these opposing views by arguing for an inverted U-shaped

relationship between slack and firm performance. Specifically, the performance benefits of slack as

advanced by behavioral theorists increase rapidly with slack, but then level off because there are only

so many valuable opportunities out there which entrepreneurs can imagine (Hambrick and

Finkelstein, 1987; Nohria and Gulati, 1997). The costs of slack are limited when slack is low, but

start to increase rapidly when slack gets beyond a certain level (Nohria and Gulati, 1997). The net

effect of these two mechanisms, an inverted U-shaped relationship, suggests that holding an

intermediate level of slack is optimal in any organizational setting (Nohria and Gulati, 1997).

Slack resources, however, do not exploit themselves and simply possessing resources is not

enough to create value out of those resources (Sirmon et al., 2007). Recently, scholars have started to

open up the black box between slack resources and firm performance by examining when, where and

how these resources influence performance. In this regard, specific attention has been paid to the role

of managers (Simsek et al., 2007). Bradley et al. (2011c), for instance, showed that managers in firms

with greater resource constraints learn to use their resources more effectively over time. An implicit

assumption in these studies is that managers have significant discretion over their resources.

According to managerial discretion theory though managers vary in how much discretion they

possess, ranging from very little to a great deal (Hambrick and Finkelstein, 1987). That this may be

important to consider was hinted at by Bradley et al. (2011a) who showed that financial slack is most

beneficial for firm performance in low-discretion environments (i.e., hostile and stable industries).

Nevertheless, much remains to be learned about factors that may condition the slack-performance

relationship.

Managerial discretion theory stipulates that the degree of managerial discretion is a function

of individual, organizational and environmental factors (Hambrick and Finkelstein, 1987). At the

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individual level, discretion is determined by the degree to which managers are able to envision

multiple courses of action (Carpenter and Golden, 1997; Hambrick and Finkelstein, 1987; Thompson,

1967). Similar to the entrepreneurial process of discovering, evaluating and exploiting opportunities

(Shane, 2000; Venkataraman, 1997), the different courses of action managers are able to envision is

largely determined by their cognitive limitations. At the organizational level, slack resources

themselves play a key role in influencing managerial discretion (Finkelstein and Hambrick, 1990;

Finkelstein and Boyd, 1998), while at the environmental level factors such as product differentiability

and capital intensity influence discretion (Finkelstein and Hambrick, 1990; Hambrick and

Abrahamson, 1995).

Most relevant to our study, and typically ignored by previous research, discretion theory

indicates that powerful stakeholders may influence managerial discretion (Hambrick and Finkelstein,

1987). Specifically, stakeholders may create discretion by raising opportunities that will not come to

mind of entrepreneurs who act alone. Thompson (1967), for instance, argues that outside investors

may help organizations in finding more domains that are profitable. Conversely, stakeholders may

constrain entrepreneurs’ deviant discretion or latitude to take action whenever such “an action lies

outside the ‘zone of acceptance’ of [those] powerful parties who hold a stake in the organization”

(Hambrick and Finkelstein, 1987, p. 374). In this study, we focus on outside investors, including VC

and angel investors, who are generally viewed as powerful stakeholders in entrepreneurial firms. We

argue that these investors will influence entrepreneurs’ discretion over resources through their active

involvement. Consequently, they are expected to influence the underlying mechanisms which drive

the performance consequences of slack resources in entrepreneurial firms. Below, we elaborate on

this claim.

How VC and Angel Investors Influence Entrepreneurial Discretion and their Impact on the

Performance Consequences of Slack Resources

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VC and angel investors are typically among the most important owners in entrepreneurial firms,

ranked second behind entrepreneurs themselves (George et al., 2005). Both types of investors are

value-adding investors or providers of “smart money” (Bruton et al., 2010; Mason and Harrison,

1996; Sapienza et al., 1996). This implies they extensively monitor the progress of their portfolio

firms and contribute value-adding services in addition to merely providing financial resources. As

such, the presence of VC and angel investors may influence an entrepreneur’s discretion over slack

resources in a number of ways.

First, entrepreneurs are limited in the number of opportunities they can perceive or create

(Shane, 2000; Shane and Venkataraman, 2000), which explains a decreasing value for each additional

amount of slack (Nohria and Gulati, 1997). VC and angel investors, however, often spend a

considerable amount of time contributing value-adding activities to portfolio firms. Gorman and

Sahlman (1989), for instance, showed that VC investors who sit on the board devote on average 80

hours of on-site time and 30 hours of phone time per year in direct contact with each of their portfolio

firms. Angel investors are known to be even more actively involved in their portfolio firms compared

to VC investors (Van Osnabrugge, 2000). Further, VC and angel investors often bring a network of

ties with potential suppliers, customers, financiers and other critical stakeholders (Hochberg et al.,

2007; Prowse, 1998). They provide entrepreneurs with a range of contacts that are typically

unavailable when entrepreneurs proceed on their own (Maurer and Ebers, 2006; Prowse, 1998). In

addition, VC and angel investors possess knowledge on the market, industry and competitors of their

portfolio firms, while entrepreneurs are often described as lacking a broader view on their markets

(Fiet, 1995; Maula et al., 2005). As a consequence of these value-adding activities, entrepreneurs

obtain access to new information, which significantly affects their ability to discover new, value-

creating opportunities (Shane and Venkataraman, 2000). By enabling entrepreneurs to consider these

opportunities that they would otherwise not have been aware of, VC and angel investors increase

entrepreneurial discretion (Thompson, 1967). Slack resources exactly benefit firm performance by

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providing entrepreneurs with the means to pursue new opportunities. Thus, VC and angel investors

are expected to amplify slack resources’ beneficial effect on firm performance by broadening

entrepreneurs’ range of valuable opportunities to pursue.

Second, both VC and angel investors will generally advocate running firms in the most

efficient way possible (Berkus, 2006; Chemmanur et al., 2011). Not only will they try to avoid the

wasteful use of their investments, they will also push entrepreneurs’ limits to make them stretch their

resources as much as possible. Board and voting rights provide VC and angel investors with the

power to enforce such entrepreneurial behavior by, for example, requiring investor ratification before

key strategic decisions can be executed (Cumming, 2008). Additionally, contracting and active

monitoring should form an important deterrent against overoptimistic behavior on the entrepreneur’s

part. To this end, investors can again rely on board and voting rights, which often include rights to

dismiss or replace the top management team (Parhankangas and Landström, 2006). Developing a

more personal relationship with entrepreneurs may be another way to avoid overoptimistic and

complacent behavior (Fiet, 1995). Forbes (2005) indeed shows that entrepreneurs whose firms have

attracted VC are less overconfident than entrepreneurs whose firms have not. As such, VC and angel

investors should decrease entrepreneurs’ deviant discretion and limit the costs of slack resources

related to lax discipline. Thus,

Hypothesis 1: The impact of (a) financial slack and (b) human resource slack on firm performance is

more positive in VC-backed firms compared to their peers without such investors.

Hypothesis 2: The impact of (a) financial slack and (b) human resource slack on firm performance is

more positive in angel-backed firms compared to their peers without such investors.

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Differences between and among VC and Angel Investors, their Influence on Entrepreneurial

Discretion, and the Performance Consequences of Slack Resources

All stakeholders in a firm will have tolerance for some actions and intolerance for others (Hambrick

and Finkelstein, 1987). Because many entrepreneurial actions will almost always lie outside the “zone

of acceptance” of some stakeholder, discretion theory indicates that scholars should focus on those

stakeholders whose withdrawal or resistance to entrepreneurial actions would pose a serious setback

to entrepreneurs (Hambrick and Finkelstein, 1987). For entrepreneurial firms, VC and angel

investors, as blockholders, are often portrayed as qualifying this condition. Blockholders are assumed

to have both the incentives and influence to affect managerial discretion (Dalton et al., 2003).

Nevertheless, treating private investors as a monolithic group of blockholders ignores some important

differences between and among these investors (e.g., Bruton et al., 2010). To get more insight into the

incentives and influence of VC and angel investors to shape entrepreneurs’ discretion, we focus on

differences between these investors and on the role of their ownership stakes.

VC versus Angel Investors. So far, we have argued that both VC and angel investors will

increase the value and decrease the costs of slack, without explicitly hypothesizing differences

between both types of investors to do so. Despite their similarities, however, VC and angel investors

also have their differences. Specifically, VC investors are professional investors that use institutional

money to invest (Sapienza, 1992). Angel investors are individuals who often have entrepreneurial

experience and use their personal funds to invest (Mason and Harrison, 1996). Due to differences in

operation modes between these two types of investors, we argue that VC investors will be better able

than angels to influence entrepreneurs’ discretion over slack resources, thereby more strongly

enhancing slack’s value while curtailing its costs.

First, VC investors invest in a larger portfolio of firms than angels among whom those with

three or more investments are a minority (Van Osnabrugge, 1998). This is important because

individuals are better able to assimilate and exploit knowledge when they have previously

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accumulated experience within related knowledge domains (Cohen and Levinthal, 1990; De Clercq

and Dimov, 2008). While angel investors often tend to invest in industries in which they have gained

some experience, the limited empirical research that is available suggests they have less industry

experience than VC investors (Van Osnabrugge, 2000). Additionally, within a VC firm one can draw

on the expertise and experience of multiple investment managers, whereas angels mainly have to rely

on themselves to provide valuable advice and information. Taken together, this suggests that VC

investors may have a broader knowledge base than angels to draw from, enabling them to bring more

opportunities to the entrepreneur’s attention and to offer them a broader perspective. As such, they

should be better able to increase the value of a given level of slack resources than angel investors.

Second, VC investors tend to write extensive contracts by putting together specific

shareholder agreements and stipulating differentiated shareholder rights (Cumming, 2008). Angel

investors, however, rely more on relational governance than contractual governance (Fiet, 1995). This

implies that angel contracts are generally more entrepreneur-friendly, have weaker control rights, use

less contractual provisions and are used more from a transactional than a control point of view

(Goldfarb et al., 2007). Prowse (1998), for instance, argues that a large proportion of the angel

community is unsophisticated in terms of ensuring adequate protection in the investment contracts

they write. Consequently, VC investors are expected to decrease the cost of slack and lax discipline

by more so than angel investors as they can rely on more professional and complete contracts to force

entrepreneurs not to invest in projects guided by overconfidence, to work more efficiently and to

avoid complacent behavior. Thus,

Hypothesis 3: The impact of (a) financial slack and (b) human resource slack on firm performance is

more positive when firms are backed by VC compared to angel investors.

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VC and Angel Investors’ Incentives through Ownership. The notion of VC and angel

investors influencing entrepreneurial discretion is rooted in these investors’ involvement in their

portfolio companies. Discretion theory indicates that to qualify as a constraint to the entrepreneur’s

discretion, investors who perceive a certain action as being outside of their “zone of acceptance” must

not only be powerful, but must also be able to react relatively immediately to the action itself

(Hambrick and Finkelstein, 1987). The latter is unlikely when investors are not sufficiently involved

in portfolio firms.

Providing extensive value-adding services and monitoring entrepreneurial actions, however,

are costly activities to investors (Manigart et al., 2002). Hence, without the proper incentives

investors may not be sufficiently motivated to be actively involved in their portfolio companies and

thus to influence entrepreneurial discretion. An important determinant of investors’ incentive to

expend effort on portfolio firms is their ownership stake (Brush et al., 2000; Zahra, 1996). A high

ownership stake should make investors more motivated and committed to provide information and

advice to entrepreneurs and to monitor their actions. When investors hold a low ownership

percentage, however, they may be less willing to bear the costs of being actively involved and will

likely be insufficiently incentivized to do so (Brush et al., 2000). Being more incentivized to provide

value-adding services, investors with a high ownership stake should increase the discretionary set of

entrepreneurs by more so than if they had a low ownership stake. Moreover, when investors have a

high ownership stake, they also have more incentives to closely monitor entrepreneurs, thereby

limiting entrepreneurs’ ability to become lax when pursuing new projects (Zahra, 1995). Together,

private investors with high ownership stakes are expected to enhance the performance consequences

of slack resources by more so than private investors with low ownership stakes as they allow

entrepreneurs to pursue more valuable opportunities with a given set of slack resources, while

reducing their ability to pursue lax investments.

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We expect this line of reasoning to hold especially true for professional VC investors given

that financial return maximization and cost efficiency is an absolute priority to them (Cumming et al.,

2007). Without the necessary incentives, they will have an inclination to prefer to execute less rather

than more costly activities, such as active post-investment involvement. Contrary to VC investors,

angels conduct investments both for their return potential and for personal motives, such as fun and

gaining prestige in the entrepreneurial community (Mason and Harrison, 2002). Angel investors are

hence likely to be more generally incentivized and more active investors irrespective of their equity

stakes (Van Osnabrugge, 2000). Thus, the impact of ownership stake on the relationship between

slack and firm performance is likely to be negligible in angel-backed firms. Professional VC

investors, on the other hand, are more likely to require a high ownership percentage to become more

actively involved in their portfolio firms. Taken together, this leads us to hypothesize:

Hypothesis 4: The impact of (a) financial slack and (b) human resource slack on firm performance is

more positive in VC-backed firms when VC investors have a high ownership stake as compared to a

low ownership stake.

Hypothesis 5: The impact of (a) financial slack and (b) human resource slack on firm performance is

more positive when VC investors have a high ownership stake as compared to when angel investors

have a high ownership stake.

METHODS

Sample and Data Sources

We use a sample of 1,215 Belgian firms. Given our research interest, we constructed a sample of

firms that raised VC or angel financing and similar firms that did not raise such financing. The

Belgian research setting provides us with two advantages. First, we had access to databases from

professional associations and networks which allowed us to construct a sample comprising both

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formal VC and angel investments. Studies comprising both VC and angel investments are rare

(Bruton et al., 2010). Second, all Belgian firms (with limited liabilities of shareholders) are required

to file financial statements with the Belgian Central bank. As such, we have detailed information on

all VC- and angel-backed firms and were able to construct a sample of similar firms which did not

raise such financing. Again, the detailed data we have available on entrepreneurial firms is rare (Brav,

2009).

We identified 120 firms backed by formal VC investors by using a database provided by the

Belgian Venture Capital and Private Equity Association (BVA). The BVA was founded in 1986 and

is the professional association that represents the Belgian VC and private equity community. The

BVA provided us a random sample of one third of all initial VC investments conducted by its

members between 1994 and 2004. It includes seed and startup investments and the provision of

growth capital, but excludes buy-outs. We also identified 90 firms backed by informal VC investors

that received initial angel financing between 1994 and 2004. For this purpose we used multiple data

sources, including deal lists from Business Angel Networks, data from the Global Entrepreneurship

Monitor and directories of high-technology firms. Additionally, for each VC- and angel-backed firm,

we searched for information on their ownership structure by collecting legally required statements on

capital increases that are published in the Belgian Law Gazette.

For each VC- and angel-backed firm, we selected up to five comparable firms which did not

raise such financing. For this purpose, we used a database comprising all financial accounts of

Belgian firms. Our matching procedure is similar to that used by Puri and Zarutskie (2012). We first

selected firms with the same age, defined as the number of years since legal incorporation because the

accumulation of slack resources and the effectiveness by which firms deploy slack is age dependent

(George, 2005). Within the group of firms founded in the same year, we selected firms operating in

the same industry based on 4-digit industry codes (or 3-digit codes whenever 4-digit codes were

unavailable). This is important because the industry environment influences the value of slack

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(Bradley et al., 2011a; George, 2005). Finally, within the group of firms founded in the same year and

operating in the same industry environment, we selected firms with a similar size because slack is

size dependent (George, 2005). Unreported statistics confirm that our samples of VC-backed firms

and their peers and angel-backed firms and their peers respectively do not differ significantly from

each other in terms of age, industry and size distribution (these unreported statistics and insights from

other matching procedures are available upon request).

Dependent Variable

We operationalize performance as gross profit, defined as the difference between sales and the cost of

goods produced (as in George, 2005). This measure is scaled by total assets to make it comparable for

firms with a different size and to reduce heteroskedasticity concerns (Brav, 2009). We do not use

performance measures based on net income because variability in the tax treatment of income in

private firms undermines the reliability of such performance estimates (George, 2005). Gross profit

on total assets is measured three years after investment (or equivalent year for the firms which do not

raise VC or angel financing) to help establish the direction of causality (Bradley et al., 2011b). It

should also allow for sufficient time for entrepreneurs to transform slack into usable resources and

address new opportunities or threats, which will subsequently influence performance. Additionally,

prior research suggests that most value added by VC investors is created in the first years after initial

investment (Bertoni et al., 2011) and that exits start to emerge from three years after the investment

(Puri and Zarutskie, 2012). As such, we believe three years represents a valid time frame for our

study.3

Independent Variables

Financial slack. We measure financial slack as the amount of cash and cash equivalents scaled by

total assets (Kim et al., 2008; Voss et al., 2008) in the year of investment for VC- or angel-backed

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firms and equivalent year for their peers. This measure is adjusted for industry norms by subtracting

the median ratio of cash and cash equivalents to total assets for all firms in the same three-digit

NACE industry as the focal firm (Bromiley, 1991; George, 2005). Thus, financial slack represents a

close estimate of excess cash resources held by firms compared to industry norms.

Human resource slack. We define human resource slack as employment cost relative to total

assets in the year of VC or angel investment and equivalent year for firms which do not raise such

financing. This measure is adjusted for industry norms by subtracting the median ratio of employment

cost on total assets for all firms in the same three-digit NACE industry as the focal firm. Larger

values indicate greater levels of human resource slack. Our measure is similar to previous studies

which defined human resource slack as the number of employees relative to sales adjusted for

industry norms (Mellahi and Wilkinson, 2010; Mishina et al., 2004; Voss et al., 2008). However, it

differs in two regards; (1) we use employment cost instead of the number of employees, and (2) we

scale employment cost by total assets rather than sales.

We use employment cost rather than the number of employees because the former forms an

integral part of the profit and loss account, while the latter is only reported in a supplementary social

balance sheet. The drawback of using the number of employees is that our sample size decreases due

to missing data. Fortunately, the available data indicates that employment cost on total assets and

number of employees on total assets are highly correlated (0.76; p < 0.001). We hence use

employment cost as a proxy for the number of employees in a firm. Moreover, employment cost not

only captures the quantity, but also the quality of employees. Employees with more human capital

(higher education and more experience) will be more costly to employ. However, individuals with

more human capital are likely to be more effective and efficient in performing a particular task

(Becker, 1964). Hence, by using employment cost rather than the number of employees we take into

account that not all employees contribute the same amount of human capital.

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We scale employment cost by total assets rather than using sales for two reasons. First, VC

investors are known to invest in high-growth, high-potential firms without immediate sales prospects,

even in low-tech industries (Puri and Zarutskie, 2012). With zero sales we would have been unable to

calculate human resource slack at the time of investment for some VC-backed firms. Second, only

large Belgian firms are required to report their sales figures. Using sales would thus lead to a bias in

that we would only be able to calculate human resource slack for these larger firms.

VC (angel) Dummy. We construct a VC (angel) dummy variable which equals one when firms

raise financing from a VC (angel) investor and zero for their peers without such financing. Prior

research indicates that the mere presence of VC (angel) investors as shareholders may influence the

operations of their portfolio firms (e.g., Ehrlich et al., 1994; Puri and Zarutskie, 2012). Further, we

calculate the interaction between the slack measures and VC (angel) dummy to study whether the

relationship between slack and performance is moderated by the presence of VC (angel) investors.

High ownership percentage. We make a distinction between VC or angel investors with a

high versus low ownership stake in their portfolio firms. To this end, we create a dummy variable

which equals one for investors with ownership stakes starting at 25% and zero otherwise. We focus

on the lead investor because this investor typically plays the most important role in the governance of

the investment and provision of value-adding services (Wright and Lockett, 2003). Further, we use a

dummy variable rather than a continuous ownership variable because it is better aligned with our

theoretical arguments. Specifically, investors (and especially VC investors) may require a sizable part

of the ownership to be sufficiently incentivized to bear the costs of being actively involved in their

portfolio firms (Brush et al., 2000). Moreover, the 25% threshold reflects an important blocking

minority, hence allowing investors to control a firm’s strategic agenda and to influence its strategic

choices (Zahra, 1995). The 25% threshold is important in Belgian legislation because as of this

threshold shareholders are able to block important corporate decisions, including decisions regarding

19

capital increases, mergers and acquisitions. We also calculate interactions between the high

ownership percentage dummy and our slack variables.

Control Variables

We control for firm, industry and year effects. With regard to firm effects, we control for firm age,

firm size, patents and other forms of slack. Firm age is measured as the years since formal

incorporation at the year of investment or equivalent year for matched firms. Firm size is measured as

the natural logarithm of property, plant, and equipment at investment or equivalent year for matched

firms. We also control for the knowledge intensity of firms by including the natural logarithm of the

cumulative number of patents applied for (plus one) up to investment or equivalent year for matched

firms. We further control for the effect of other forms of slack. Potential slack is measured as the

debt-to-equity ratio adjusted for industry norms at investment or equivalent year for matched firms

(George, 2005). Recoverable slack is measured as the sum of accounts receivables and inventory on

total assets adjusted for industry norms (Bradley et al., 2011a) and is also measured at investment or

equivalent year for matched firms.

There exists non-randomness in the probability that firms raise VC or angel financing (Cosh

et al., 2009). Specifically, entrepreneurs search for (or eschew) certain outside investors and/or

outside investors seek out certain types of firms. If studies fail to control for such selection issues

regressions may produce biased results (Cosh et al., 2009). To address this possibility, we include an

Inverse Mills Ratio or selection correction as a control. Following prior research (Cosh et al., 2009),

we employed the Heckman procedure. First, we develop selection models predicting the receipt of

VC or angel financing respectively using insights from previous research on financing decisions in

entrepreneurial firms (Cosh et al., 2009; Vanacker and Manigart, 2010). Specifically, cash flow to

total assets, intangible assets to total assets, the natural logarithm of the number of patents applied for

(plus one), tangible assets to total assets, overdue short term priority debt on total assets, debt-to-

20

equity ratio, interest coverage ratio and the natural logarithm of firm size were used in the selection

models. The ratio of intangible assets to total assets, for instance, is described as a good proxy for

growth potential (Villalonga, 2004) and is related to the probability of raising outside equity finance

(Vanacker and Manigart, 2010). The number of patents applied for is another variable on which

especially VC investors are likely to select (Hsu and Ziedonis, 2008). Second, a correction is made

for selection by incorporating the Inverse Mills Ratio obtained from the selection models into the

regression models of interest.4

With regard to industry effects, we control for industry profitability measured as the natural

logarithm of median gross profit based on all firms in the same three-digit NACE industry as the

focal firms. High industry profitability is not only likely to influence the performance level of the

focal firms, but may also indicate opportunities to generate slack (George, 2005). We further included

industry dummy variables to capture broader industry-level effects: manufacturing, construction,

wholesale and retail trade, information and communication technologies (ICT), biotechnology,

business services and other. We used ICT as the reference industry.

Finally, we include dummy variables for the years in which firms received financing from VC

or angel investors and the years in which their peers entered our dataset. These variables help to

control for the effects of any general economic event or trend.

RESULTS

Descriptive statistics

Table I provides an overview of the means, standard deviations and correlations between the

variables used in the empirical models. Panel A focuses on the sample of VC-backed firms and their

peers without such investors, while Panel B focuses on the sample of angel-backed firms and their

peers without such investors. We note that both for VC-backed firms and their peers and angel-

backed firms and their peers correlations between financial and human resource slack are low and

21

insignificant. This indicates that firms with financial slack do not necessarily have human resource

slack or vice versa.

***INSERT TABLE I ABOUT HERE***

Hypothesis tests

Table II gives the results of the OLS regressions using the sample of VC-backed firms and their

peers, and angel-backed firms and their peers respectively. Model 1 and 2 are the baseline models

without interactions. In Model 3, we focus on VC-backed firms and their peers and add interactions

between the VC dummy and our slack measures. This model is used to test Hypothesis 1. In Model 4,

we focus on angel-backed firms and their peers and add interactions between the angel dummy and

our slack measures. This model is used to test Hypothesis 2. Variance inflation factors (VIFs) in all

models are well below 10 and hence do not indicate that multicollinearity may be unduly influencing

our results (Kutner et al., 2005).

***INSERT TABLE II ABOUT HERE***

Hypothesis 1 stated that the impact of (a) financial slack and (b) human resource slack on

performance would be more positive in VC-backed firms compared to their non-VC-backed peers.

The moderating effect of VC on the financial slack-performance relationship is significant and

positive in the main effects interaction from Model 3 (β = 0.057; p < 0.10). As illustrated in Figure 1,

the relationship between financial slack and performance is more positive in VC-backed firms than in

non-VC-backed firms. With regard to human resource slack, although the VC-human resource slack

interaction is not significant, the interaction between the VC dummy and the squared term of human

resource slack is positive and significant (β = 0.038; p < 0.05). Figure 2 shows that an increase in

human resource slack increases performance in a linear fashion in non-VC-backed firms, but in an

22

exponential fashion in VC-backed firms. Hence, a given increase in human resource slack results in a

stronger increase in performance for VC-backed firms. Overall, we find support for Hypothesis 1a

and 1b.

***INSERT FIGURE 1 AND FIGURE 2 ABOUT HERE***

Hypothesis 2 stated that the impact of (a) financial slack and (b) human resource slack on

performance would be more positive in angel-backed firms than in their non-angel-backed peers.

Model 4 shows that the presence of angel investors does not influence the relationship between

financial slack and firm performance, but it does alter the relationship between human resource slack

and performance. Specifically, the angel dummy and human resource slack interaction is positive and

significant (β = 0.042; p < 0.05). As illustrated in Figure 3, an increase in human resource slack is

more positive for performance in angel-backed firms compared to non-angel-backed firms. Thus,

while we fail to find support for Hypothesis 2a, we do find support for Hypothesis 2b.

***INSERT FIGURE 3 ABOUT HERE***

Hypothesis 3 stated that the impact of (a) financial slack and (b) human resource slack on

performance would be more positive when firms are backed by VC investors as compared to angel

investors. While qualitative comparisons of the coefficients between Model 3 and Model 4 are

instructive, they do not provide a formal statistical test for our hypothesis. We use seemingly

unrelated estimation (Suest) to formally test for differences in the size of the coefficients across

regression models (Wade et al., 2006). We find no statistical difference for the moderating role of VC

investors versus angel investors on the relationship between financial slack and firm performance.

Our results show that the interaction effect between the VC dummy and human resource slack

squared in Model 3 is significantly greater as compared to the interaction between the angel dummy

23

and human resource slack squared in Model 4 (p < 0.10). Thus, we find no support for Hypothesis 3a

and weak support for Hypothesis 3b.

Table III reports the OLS regressions on the sample of only VC-backed firms and angel-

backed firms respectively. VIFs indicated that multicollinearity was a concern for our slack measures

and their interaction with the high ownership dummy variable. We therefore employed the “orthog”

command in Stata to generate orthogonalized measures. This technique “partials out” the common

variance, creating transformed variables that are uncorrelated with one another (Bradley et al., 2011a;

Pollock and Rindova, 2003). Although this procedure makes direct interpretation of coefficients more

difficult, it still allows for evaluation of the strength and direction of relationships (Pollock and

Rindova, 2003). After this procedure, all VIFs were well below the critical threshold, indicating that

multicollinearity is no longer a concern in the reported models. Models 5 and 6 are baseline models,

which only include main effects. In Model 7 and 8, we add the interaction effects to study the

moderating impact of a high ownership percentage on the relationship between slack and

performance.

Hypothesis 4 stated that in VC-backed firms the impact of (a) financial slack and (b) human

resource slack on performance would be more positive when investors have a high ownership

percentage. Based on the sample of VC-backed firms only, model 7 shows that the product term

between financial slack and the high ownership dummy variable is positive and significant (β =

0.068; p < 0.01). Figure 4 shows that the relationship between financial slack and performance is

more positive in VC-backed firms when the lead investor has a high ownership stake. Further, we

find that the moderating effect of high VC ownership stake on the human resource slack-performance

relationship is significant and positive in the main effects interaction from model 7 (β = 0.089; p <

0.01), while marginally significant and positive for the squared human resource slack-high ownership

stake interaction term (β = 0.050; p < 0.10). As Figure 5 illustrates, the relationship between human

24

resource slack and firm performance is significantly steeper in firms backed by a VC investor with a

high ownership stake. Overall, we find strong supporting evidence for Hypotheses 4a and 4b.

***INSERT FIGURE 4 AND FIGURE 5 ABOUT HERE***

Hypothesis 5 stated that the impact of (a) financial slack and (b) human resource slack on

performance would be more positive when VC investors have a high ownership stake as compared to

when angel investors have a high ownership stake. We indeed fail to find an impact of high

ownership stakes on the relationship between slack and performance in the sample of angel-backed

firms (Model 8). Nevertheless, as argued above, qualitative comparisons of the coefficients between

Model 7 and Model 8 do not provide a formal test of our hypothesis. We use seemingly unrelated

estimation (Suest) to formally test for differences in the size of the coefficients across regression

models. Our results show that the interaction between financial slack and a high VC ownership

percentage in Model 7 is significantly greater as compared to the interaction between financial slack

and a high angel ownership percentage in Model 8 (p < 0.05). In addition, the interaction between

human resource slack and a high VC ownership percentage in Model 7 is significantly greater as

compared to the interaction between human resource slack and a high angel ownership percentage in

Model 8 (p < 0.01). This provides strong supporting evidence for Hypothesis 5a and 5b.

DISCUSSION AND CONCLUSION

In this study, we considered how powerful external stakeholders influence the relationship between

slack resources and firm performance. We find that the presence of VC and angel investors positively

moderates the slack-performance relationship. Additionally, this moderating effect is conditioned by

the type of investor under consideration (i.e. VC versus angel) and investor ownership stake (i.e. high

25

versus low). Our study contributes to the management and entrepreneurship literatures in multiple

ways.

First, we add to the slack literature by further unpacking the black box between slack

resources and firm performance. Specifically, our findings underscore the importance of considering

outside investors in debates regarding the value of resource slack in private firms given that these

investors are likely to influence the discretion by which entrepreneurs can use their slack resources,

which subsequently affects the performance consequences of these resources. To illustrate the value

of investors for the slack-performance relationship, consider the following numbers. VC-backed firms

showed a 1.40 times increase in performance from minus one to plus one standard deviation of

financial slack compared to a 1.03 times increase for their peers without such investors. An increase

from minus one to plus one standard deviation of human resource slack benefits the performance of

VC-backed firms more than the performance of similar firms without such investors (2.22 versus 2.20

times increase), where the benefit is especially important for VC-backed firms when human resource

slack moves beyond the mean level (see Figure 2). An increase from minus one to plus one standard

deviation in human resource slack had a greater impact on firm performance for angel-backed firms

compared to their peers without such investors (2.50 versus 1.75 times increase) (see Figure 3).

We also contribute to the slack literature by focusing on both financial and human resource

slack, whereas prior studies largely focused on the former (e.g., George, 2005; Bradley et al., 2011b).

This is important as we note some differences in the performance consequences of these two types of

slack. Unreported results (using the full sample of private firms) show that the linear effect of human

resource slack on performance is more positive than that of financial slack (p < 0.001). We fail to find

a difference with regard to quadratic effects. This entails that an increase in human resource slack has

a more positive effect on firm performance compared to financial slack. An important avenue for

future research is to increase our understanding why different types of resource slack have a different

impact on firm performance. Additionally, it raises some concerns regarding measures that combine

26

financial and human resource slack (e.g., Nohria and Gulati, 1996) as such scales may mask key

differences between different types of slack.

Second, we extend the managerial discretion literature by exploring the idea that powerful

parties may enhance or restrict discretion. Most discretion research has focused on the internal

discretion of managers but has largely ignored the ideas that (1) discretion may be distributed across

organizational boundaries, with powerful actors that are not managers of the firm and (2) that these

powerful outside actors may also serve to enhance rather than just restrict managerial discretion, both

of which we support with our study.

Third, our study contributes to governance and slack research by examining the role of

investor type and ownership stake in the slack-performance relationship. As for the former, we find

only weak evidence that VC investors are better than angel investors at pushing entrepreneurs to

extract more value out of a given set of human resource slack and no evidence that they are better at

extracting value out of financial slack. These weak results may be explained by the huge

heterogeneity among these investor groups. Specifically, whereas we argued that VC investors on

average have a broader knowledge base to draw from, we must acknowledge that this assumes both

VC and angel investors to be rather homogeneous groups of investors with regard to their relevant

experience. However, previous research has indicated that substantial variation exists among both

groups with regard to their experience related to industries they invest in and to making investments

as such (e.g., De Clercq and Dimov, 2008; Dimov and De Clercq, 2006; Wiltbank, 2005). Moreover,

we know these differences in experience matter; for instance, angel investors tend to achieve higher

returns whenever they do invest in areas they have expertise in (Wiltbank and Boeker, 2007). A

highly experienced angel investor may hence serve an entrepreneur equally well as a highly

experienced VC investor when it comes to helping them to see opportunities they would otherwise

not have seen. Getting a clear picture on how VC and angel investors may differentially affect the

slack-performance relationship may thus require a more detailed measurement of our hypothesized

27

underlying mechanisms (e.g., their knowledge base), thereby allowing researchers to take into

account the significant heterogeneity that exists among both groups of investors.

Another fruitful avenue for future research would be to measure the type of assistance

provided by these investors. This may allow us to answer the question of why VC investors are

marginally better than angel investors at helping entrepreneurs creating value out of their human

resources, but not out of their financial resources (see Table II). A possible explanation may be found

in some of the early research on differences between VC and angel investors. Ehrlich et al. (1994) for

instance suggested that angel investors are less likely to change the make-up of their portfolio

companies’ management teams and are less likely to provide assistance regarding staffing issues

(whereas entrepreneurs do rely on both types of investors to provide assistance in financial issues).

The difference may hence not so much be in the level of value-adding provided by VC and angel

investors, but rather in the type of value-adding provided.

Despite the weak results related to differences between VC and angel investors with regard to

their influence on the slack - performance relationship as such, we do find an important difference

when we take into account ownership stakes. Specifically, results suggest that especially professional

VC investors act as a source of resource value creation when they have high ownership stakes in their

portfolio firms (Figure 4 and Figure 5). Having a high ownership stake resulted in a 1.78 times

increase in firm performance from minus one to plus one standard deviation in financial slack for VC-

backed firms while having a low ownership stake resulted in a 1.42 times increase in firm

performance from minus one to plus one standard deviation in financial slack. For human resource

slack, an increase from minus one to plus one standard deviation had a greater impact on firm

performance for firms backed by VC investors with a high ownership stake (3.04 times increase)

compared to those with a low ownership stake (2.38 times increase). We fail to find an impact of

angel ownership on the relationship between slack resources and firm performance. These results tie

into a significant stream of governance research which has focused on the relationship between

28

ownership and firm performance. Our findings show that for VC investors high ownership stakes

matter, but not so for angel investors. Together these findings thus reveal more complex links

between investor type, ownership concentration and the performance consequences of slack resources

in private firms. The typical assumption in extant governance research that outside blockholders of

equity are by definition incentivized and able to monitor their investments is hence unwarranted.

Finally, our study contributes to the entrepreneurial finance literature. While research on the

value-adding or coaching role of VC investors is rather elaborate (e.g., Sapienza, 1992; Sapienza et

al., 1996), few studies have demonstrated the processes by which VC investors increase firm

performance. We argue that VC investors, through their value-adding and monitoring roles, help

entrepreneurial firms to allocate their slack resources in a way that increases firm performance. This

is especially the case for VC investors when they have a high ownership stake in their portfolio firms.

Second, we not only focus on VC investors, but also on angel investors. While there is an extensive

literature on VC financing, the literature on angel financing is more limited. This is unfortunate as the

angel investment market is expected to be multiple times larger than the venture capital market

(Mason and Harrison, 1996). Although our study shows similarities between VC and angel investors,

it also points towards differences between these private investors as sources of resource value

creation.

Our paper also has several practical implications. First, entrepreneurs may learn about the

optimal level of financial and human resources in their firms. Further, one of the main incentives for

entrepreneurs to search for VC or angel financing is a need for financial resources to support their

high-growth ambitions. Our findings, however, show that these investors do not necessarily provide

their portfolio firms with more slack resources, but that these investors do help entrepreneurs to make

the most out of the resources at hand. Second, results are interesting for VC and angel investors as

well. Our findings demonstrate that both investor types are able to (at least partially) reduce the

negative consequences of holding too much slack. VC and angel investors typically have the difficult

29

task of deciding how many resources they will contribute to their portfolio firms without stimulating

entrepreneurs to become less creative or pursue dubious investments. Our study demonstrates that

especially in VC-backed firms, where these investors have a large ownership stake, the performance

consequences of having a buffer of financial resources is largely positive. VC investors should hence

be careful not to provide their portfolio firms with too little resources (for instance by using excessive

staged financing) in an effort to reduce potential agency problems.

Conclusion

Previous research on the performance consequences of slack resources in private firms ignored a

critical reality: many entrepreneurs do not “go it alone” when pursuing their ambitions but rather

obtain support from outside investors, such as VC and angel investors. In this study, we have

explored how different ownership contexts influence the performance consequences of slack

resources in private firms. We argued that VC and angel investors influence entrepreneurs’ discretion

over their slack resources, which is expected to influence the performance consequences of these

resources. Specifically, we argued and showed that both VC and angel investors increase the value

(decrease the cost) of slack resources. VC investors with a high ownership stake are especially

instrumental as a source of resource value creation. Our study points to complex links between

different types of outside investors, slack resources, and the performance of private firms.

ACKNOWLEDGEMENTS

We thank three anonymous JMS reviewers, Donald Bergh (the editor), Dan Forbes, Mirjam

Knockaert, Ian MacMillan and James Thompson among others, for their helpful feedback. This paper

further benefited from seminars at The Sol C. Snider Entrepreneurial Research Center (The Wharton

School), Eindhoven University of Technology, Université Catholique de Louvain and Maastricht

30

University. Tom Vanacker acknowledges the financial support of the Research Foundation Flanders

(Grant FWO11/PDO/076) and the Hercules Fund.

NOTES

1. We thank an anonymous reviewer for drawing our attention to this contribution.

2. Scholars have also used agency theory as a framework to explain the negative performance

consequences of slack resources (e.g., Nohria and Gulati, 1997). Several scholars, however, have argued

that agency issues are minimized in private firms because entrepreneurs are typically the most important

owners (George, 2005). This is even the case for private firms that raise outside equity finance. Moreover,

private investors use several mechanisms to minimize potential agency issues (Gompers, 1995). Although

agency arguments with respect to the misuse of slack may not hold for the type of firms we study, the core

assumption of a possible negative relationship between slack and firm performance remains valid (Mellahi

and Wilkinson, 2010). Indeed, while there is no apparent concern regarding the objectives of

entrepreneurs, they may still have discretion to take actions that lie outside the “zone of acceptance” of

investors and invest in dubious projects. Entrepreneurs, for instance, may take actions with the intent to

create value, but which are driven by overconfidence. This demonstrates an important distinction between

“latitude of objectives” and “latitude of actions” (Hambrick and Finkelstein, 1987), where our study is

mainly concerned with the latter.

3. All Belgian firms are required to report gross profit, irrespective of their size. Small firms, however,

only need to provide gross profit and not the individual components (including sales and cost of goods

produced). Also note that our results are robust for alternative time lags in measuring firm performance

(two and four years).

4. Obviously, one can never rule out selection issues entirely and other selection issues may exist. For

instance, the “best” entrepreneurial firms may match with the “best” investors. To address this possibility,

we added controls reflecting VC investor quality (such as VC age and a dummy for the oldest, most

31

established Belgian VC investor) in our regression models including VC-backed firms. Results did not

change. Unfortunately, we do not have such data for angel investors.

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Table I Descriptive statistics and correlation matrix

Panel A: VC-backed firms and their peers (n=631)

Panel B: Angel-backed firms and their peers (n=446)

Correlations significant at p < 0.05 are in bold. Variables 7 and 8 are binary thus their correlations should be interpreted with care.

Variable Mean S.D. Mean S.D. 1 2 3 4 5 6 7 8 9 101 Firm age 3.76 5.53 3.97 5.66 1.002 Firm size 3.64 1.87 3.56 2.00 0.22 1.003 Patents 0.02 0.13 0.02 0.15 0.10 0.06 1.004 Potential slack 6.28 12.99 4.52 10.79 0.00 0.06 0.01 1.005 Absorbed slack -0.08 0.30 -0.08 0.28 0.18 -0.23 0.06 0.12 1.006 Industry profitability 5.96 0.61 5.95 0.58 -0.24 0.04 0.02 -0.10 -0.13 1.007 Angel dummy 1.00 0.00 0.00 0.00 -0.01 0.02 0.02 0.06 0.00 0.01 1.008 High ownership % 0.58 0.50 N.A. N.A. -0.27 -0.02 -0.14 -0.03 -0.16 0.00 N.A. 1.009 Financial slack 0.10 0.23 0.09 0.21 -0.08-0.25 -0.03 -0.26 -0.30 -0.02 0.03 0.25 1.00

10 Human resource slack -0.08 0.29 -0.10 0.30 0.04 0.01 -0.03 0.06 0.03 0.01 0.03 0.07 0.02 1.0011 Profitability 0.39 0.39 0.39 0.35 0.06 0.09 -0.03 0.050.16 -0.07 0.00 -0.22 -0.06 0.27

Angel-backed firms Peer group Correlations

Variable Mean S.D. Mean S.D. 1 2 3 4 5 6 7 8 9 10

1 Firm age 6.23 8.65 6.25 8.22 1.002 Firm size 4.37 2.59 3.95 2.12 0.29 1.00 3 Patents 0.10 0.44 0.01 0.12 0.08 0.12 1.004 Potential slack 2.85 9.76 4.80 10.24 -0.07 0.06 0.02 1.005 Absorbed slack -0.20 0.26 -0.07 0.29 0.13 -0.15 -0.06 0.18 1.006 Industry profitability 5.93 0.58 5.92 0.58 0.02 0.10 0.10 -0.06 0.04 1.00 7 VC dummy 1.00 0.00 0.00 0.00 0.00 0.07 0.15 -0.07 -0.16 0.01 1.008 High ownership % 0.52 0.50 N.A. N.A. -0.05 -0.16 -0.24 -0.14 0.01 -0.23 N.A. 1.009 Financial slack 0.12 0.24 0.08 0.20 -0.06 -0.22 0.07 -0.21 -0.30 0.01 0.07 0.15 1.00

10 Human resource slack -0.11 0.25 -0.09 0.28 0.13 0.04 0.01 0.01 0.21 0.07 -0.03 0.17 -0.05 1.00 11 Profitability 0.34 0.34 0.40 0.36 0.09 0.05 -0.03 -0.02 0.24 0.02 -0.06 0.12 -0.04 0.47

VC-backed firms Peer group Correlations

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Table II The performance consequences of slack resources in VC-backed firms, angel-backed firms and their respective peers a

a Industry and year controls are included but not reported. Unstandardized coefficients are reported; standard errors adjusted for heteroskedasticity are in parentheses. † 0.10; * p < 0.05; ** p < 0.01 and *** p < 0.001 (in the regression models, one-tailed tests are used for hypothesized effects, two-tailed tests for control variables).

Diff. Model 3 vs. 4

Firm age 0.001 (0.002) 0.001 (0.003) 0.001 (0.002) 0.001 (0.003)

Firm size 0.015* (0.006) 0.023* (0.010) 0.015* (0.007) 0.021* (0.010)Patents 0.022 (0.052) -0.099* (0.040) 0.036 (0.054) -0.100** (0.038) *

Potential slack 0.007† (0.004) -0.003† (0.002) 0.007† (0.004) -0.004* (0.002) *

Potential slack squared 0.000† (0.000) 0.000** (0.000) 0.000† (0.000) 0.000** (0.000) **

Recoverable slack 0.198*** (0.055) 0.275** (0.081) 0.180** (0.055) 0.245** (0.082)

Recoverable slack squared -0.374** (0.132) -0.229 (0.163) -0.400** (0.130) -0.256 (0.162)

Industry profitability -0.067* (0.026) -0.016 (0.026) -0.065* (0.026) -0.024 (0.026)

Inverse Mills Ratio 0.134*** (0.038) 0.046 (0.040) 0.143*** (0.038) 0.031 (0.041) *

Financial slack 0.027 (0.031) 0.073** (0.027) 0.005 (0.036) 0.061* (0.027)

Financial slack squared -0.024 (0.028) -0.063* (0.028) -0.008 (0.033) -0.051† (0.030)

Human resource slack 0.164*** (0.016) 0.104*** (0.017) 0.164*** (0.017) 0.087*** (0.019) **

Human resource slack squared -0.003 (0.018) -0.060** (0.019) -0.015 (0.019) -0.056* (0.021)

Venture capital (VC) dummy 0.027 (0.031) -0.040 (0.043)

Angel dummy -0.041 (0.047) 0.035 (0.065)

Financial slack x VC (angel) dummy 0.057† (0.036) 0.038 (0.050)

Financial slack squared x VC (angel) dummy -0.046 (0.036) -0.053 (0.048)

Human resource slack x VC (angel) dummy 0.005 (0.017) 0.042* (0.021)

Human resource slack squared x VC (angel) dummy 0.038* (0.019) -0.019 (0.027) †

Constant 0.657*** (0.183) 0.532** (0.178) 0.635*** (0.182) 0.585*** (0.177)

F-statistic 9.320 4.84 8.470 4.520

Prob. 0.000 0.000 0.000 0.000

R-squared 0.284 0.206 0.326 0.272

N 631 446 631 446

Model 1 Model 3 Model 4

VC-backed firms and peer group

VC-backed firms and peer group

Angel-backed firms and peer group

Model 2

Angel-backed firms and peer group

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Table III Slack resources, performance and the ownership stake of VC investors and angel investors a

a Industry and year controls are included but not reported. Unstandardized coefficients are reported; standard errors adjusted for heteroskedasticity are in parentheses. † 0.10; * p < 0.05; ** p < 0.01 and *** p < 0.001 (in the regression models, one-tailed tests are used for hypothesized effects, two-tailed tests for control variables).

Diff. Model 7 vs. 8

Firm age 0.001 (0.004) -0.003 (0.008) -0.001 (0.003) -0.001 (0.008)

Firm size 0.050*** (0.012) 0.012 (0.037) 0.047*** (0.011) -0.008 (0.035)

Patents 0.122 (0.084) -0.755* (0.315) 0.178* (0.079) -0.686* (0.310) **

Potential slack -0.001 (0.006) -0.009 (0.006) 0.002 (0.006) -0.007 (0.007)

Potential slack squared 0.000 (0.000) 0.000 (0.000) 0.000 (0.000) 0.000 (0.000)

Recoverable slack 0.525** (0.169) 0.313 (0.264) 0.586** (0.168) 0.216 (0.230)

Recoverable slack squared 0.471 (0.315) -0.228 (0.397) 0.366 (0.265) -0.137 (0.414)

Industry profitability -0.033 (0.064) -0.062 (0.084) -0.080 (0.063) -0.047 (0.078)

Inverse Mills Ratio 0.295** (0.092) 0.469* (0.205) 0.301** (0.088) 0.410* (0.200)

Financial slack 0.054† (0.031) 0.022 (0.076) 0.064* (0.028) 0.008 (0.076)

Financial slack squared -0.048† (0.027) -0.071 (0.067) -0.020 (0.026) -0.065 (0.060)

Human resource slack 0.180** (0.053) 0.122 (0.076) 0.201*** (0.040) 0.156† (0.081)

Human resource slack squared 0.074† (0.042) -0.013 (0.053) 0.073* (0.036) -0.001 (0.053)

High ownership % 0.044 (0.033) -0.051 (0.054) 0.043 (0.028) -0.053 (0.051)†

Financial slack x High ownership % 0.068** (0.028) -0.078 (0.058) *

Financial slack squared x High ownership % 0.034 (0.030) 0.074 (0.061)

Human resource slack x High ownership % 0.089** (0.030) -0.065 (0.043) **

Human resource slack squared x High ownership % 0.050† (0.031) -0.032 (0.050)

Constant 0.178 (0.499) 0.173 (0.653) 0.528 (0.466) 0.205 (0.652)

F-statistic 3.350 1.570 3.900 1.440

Prob. 0.000 0.103 0.000 0.151

R-squared 0.554 0.484 0.637 0.528

N 101 65 101 65

Model 5 Model 6 Model 7 Model 8

Only VC-backed firms

Only angel-backed firms

Only VC-backed firms

Only angel-backed firms

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Figure 1. Moderating effect of VC investors on the relationship between financial slack and firm

performance

Figure 2. Moderating effect of VC investors on the relationship between human resource slack and

firm performance

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Figure 3. Moderating effect of angel investors on the relationship between human resource slack

and firm performance

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Figure 4. Moderating effect of VC ownership percentage on the relationship between financial slack

and firm performance for VC-backed firms

Figure 5. Moderating effect of VC ownership on the relationship between human resource slack and

firm performance for VC-backed firms

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High ownership% (25% or more) Low ownership% (less than 25%)